Enable dequantization from MXFP8 tensor with only columnwise data

tests/pytorch/test_quantized_tensor.py

@@ -656,3 +656,86 @@ def test_chunk(
             tols = dict(rtol=0, atol=0)  # Chunking is exact
             y_test = y_test.to(dtype=torch.float64, device="cpu")
             torch.testing.assert_close(y_test, y_ref, **tols)
+
+
+@pytest.mark.skipif(not mxfp8_available, reason=reason_for_no_mxfp8)
+class TestMXFP8Tensor:
+
+    @staticmethod
+    def setup_class(cls) -> None:
+        # Configure RNG
+        seed = 1234
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+
+    @pytest.mark.parametrize("fp8_dtype", _fp8_dtypes)
+    @pytest.mark.parametrize("dtype", _dtypes)
+    @pytest.mark.parametrize("dims", [[128, 128], [256, 256], [128, 256]])
+    def test_mxfp8_dequantize_columnwise_only(
+        self,
+        fp8_dtype: tex.DType,
+        dtype: torch.dtype,
+        dims: DimsType,
+    ) -> None:
+        """Check dequantization of MXFP8 tensor with only columnwise data"""
+
+        # Initialize random data
+        x_ref = 2 * torch.rand(_to_list(dims), dtype=dtype, device="cuda") - 1
+
+        # Quantize with both rowwise and columnwise
+        quantizer = MXFP8Quantizer(fp8_dtype=fp8_dtype, rowwise=True, columnwise=True)
+        x_mxfp8 = quantizer(x_ref)
+
+        # Dequantize from rowwise (default path)
+        x_deq_rowwise = x_mxfp8.dequantize(dtype=dtype)
+
+        # Rowwise dequantization should be close to the original
+        torch.testing.assert_close(x_deq_rowwise, x_ref, **_tols[fp8_dtype])
+
+        # Strip rowwise data, keeping only columnwise
+        x_mxfp8.update_usage(rowwise_usage=False, columnwise_usage=True)
+        assert x_mxfp8._rowwise_data is None
+        assert x_mxfp8._columnwise_data is not None
+
+        # Dequantize from columnwise only
+        x_deq_columnwise = x_mxfp8.dequantize(dtype=dtype)
+
+        # Columnwise dequantization should be close to the original
+        torch.testing.assert_close(x_deq_columnwise, x_ref, **_tols[fp8_dtype])
+
+        # Rowwise and columnwise dequantizations should match each other
+        torch.testing.assert_close(x_deq_columnwise, x_deq_rowwise, **_tols[fp8_dtype])
+
+        # Make sure we are not trivially passing the test
+        with pytest.raises(AssertionError):
+            torch.testing.assert_close(x_deq_columnwise, -x_ref, **_tols[fp8_dtype])
+
+    @pytest.mark.parametrize("fp8_dtype", _fp8_dtypes)
+    @pytest.mark.parametrize("dims", [[128, 128], [256, 256]])
+    def test_mxfp8_dequantize_columnwise_only_quantized_separately(
+        self,
+        fp8_dtype: tex.DType,
+        dims: DimsType,
+    ) -> None:
+        """Check dequantization of MXFP8 tensor quantized with columnwise only"""
+
+        dtype = torch.bfloat16
+
+        # Initialize random data
+        x_ref = 2 * torch.rand(_to_list(dims), dtype=dtype, device="cuda") - 1
+
+        # Quantize with columnwise only (no rowwise)
+        quantizer = MXFP8Quantizer(fp8_dtype=fp8_dtype, rowwise=False, columnwise=True)
+        x_mxfp8 = quantizer(x_ref)
+        assert x_mxfp8._rowwise_data is None
+        assert x_mxfp8._columnwise_data is not None
+
+        # Dequantize from columnwise only
+        x_deq = x_mxfp8.dequantize(dtype=dtype)
+
+        # Should be close to the original
+        torch.testing.assert_close(x_deq, x_ref, **_tols[fp8_dtype])
+
+        # Make sure we are not trivially passing the test
+        with pytest.raises(AssertionError):
+            torch.testing.assert_close(x_deq, -x_ref, **_tols[fp8_dtype])

transformer_engine/pytorch/tensor/storage/mxfp8_tensor_storage.py

@@ -33,9 +33,9 @@ def forward(
         dtype = torch_to_transformer_engine_dtype[dtype]
 
         # Make sure FP8 data is in expected format
-        if tensor._rowwise_data is not None:
+        if tensor._rowwise_data is not None or tensor._columnwise_data is not None:
             return tex.dequantize(tensor, dtype)
-        raise NotImplementedError("Casting back from the transpose not implemented yet!")
+        raise ValueError("Cannot dequantize MXFP8 tensor with no data")
 
     @staticmethod
     def backward(